MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

Shi, Jiaxin; Dong, Yue; Jiang, Wenyan; Qin, Fengying; Wang, Xiaoyu; Cui, Lixia; Liu, Yan; Jin, Ying; Luo, Yahong; Jiang, Xiran

doi:10.1016/j.mri.2021.12.008

Cited by 32 publications

(23 citation statements)

References 31 publications

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“…Combined radiomics models with tumor and peritumoral 3 mm regions further improved the performance in both T2WI and DWI in this study with a highest AUC of 0.868 and 0.846 achieved in the training and testing sets, respectively. This is consistent with the study of Shi et al [ 23 ] in which both intra- and peritumoral regions of contrast-enhanced T1WI and T2WI were combined to achieve an AUC of 0.830 and 0.853.…”

Section: Discussionsupporting

confidence: 92%

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Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

et al. 2023

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Background Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM. Methods A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM. Results The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC. Conclusions Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.

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Section: Discussionsupporting

confidence: 92%

“…A mask of the area with tumor boundary was defined as the intra-tumoral region. The peritumoral ROIs were obtained with python (version 3.7.6) by uniform expansion of intra-tumoral region with a dimension of 3 mm, 5 mm, 7 mm, respectively [ 23 , 24 ]. The tumor boundary to the outer expansion boundary was defined as peri-tumoral region.…”

Section: Methodsmentioning

confidence: 99%

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

et al. 2023

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“…However, peritumoral regions in this study were defined the region along the mesorectal fascia and the outer edge of the tumor and rectal wall. Several previous studies in other tumors indicated that peritumoral regions were defined as the area immediately surrounding the tumor ( 18 , 20 , 32 , 33 ). Some studies also reported metabolic changes in the peritumoral region, including increased uptake of FDG by the tissues adjacent to the tumor compared with distant tissues ( 34 , 35 ).…”

Section: Discussionmentioning

confidence: 99%

“…Some studies also reported metabolic changes in the peritumoral region, including increased uptake of FDG by the tissues adjacent to the tumor compared with distant tissues ( 34 , 35 ). Therefore, we chose a 3-mm area around the tumor boundary as the peritumoral region according to previous studies ( 18 , 20 ).…”

Section: Discussionmentioning

confidence: 99%

“…It is the interaction between tumor cells and the surrounding microenvironment that influences tumor evolution and progression ( 15 ). Several studies have shown that radiomics based on peritumoural regions improves the diagnostic performance for identifying LNM in other cancers ( 16 – 18 ). Therefore, we can presume that radiomics derived from intratumoral and peritumoral regions could also predict LNM in rectal cancer.…”

Section: Introductionmentioning

confidence: 99%

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MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer

Chen

Liu

et al. 2023

Front. Oncol.

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PurposeTo establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with resectable rectal cancer.MethodsA total of 346 patients with pathologically confirmed rectal cancer from two hospitals between January 2019 and December 2021 were prospectively enrolled. Intra- and peritumoral features were extracted separately, and least absolute shrinkage and selection operator regression was applied for feature selection. Radiomics signatures were built using the selected features from different regions. The clinical-radiomic nomogram was developed by combining the intratumoral and peritumoral radiomics signatures score (radscore) and the most predictive clinical parameters. The diagnostic performances of the nomogram and clinical model were evaluated using the area under the receiver operating characteristic curve (AUC). The prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis.ResultsThe intratumoral radscore (radscore 1) included four features, the peritumoral radscore (radscore 2) included five features, and the combined intratumoral and peritumoural radscore (radscore 3) included ten features. The AUCs for radscore 3 were higher than that of radscore 1 in training cohort (0.77 vs. 0.71, P=0.182) and internal validation cohort (0.76 vs. 0.64, P=0.041). The AUCs for radscore 3 were higher than that of radscore 2 in training cohort (0.77 vs. 0.74, P=0.215) and internal validation cohort (0.76 vs. 0.68, P=0.083). A clinical-radiomic nomogram showed a higher AUC compared with the clinical model in training cohort (0.84 vs. 0.67, P<0.001) and internal validation cohort (0.78 vs. 0.64, P=0.038) but not in external validation (0.72 vs. 0.76, P=0.164). Multivariate Cox analysis showed MRI-reported extramural vascular invasion (EMVI) (HR=1.099, 95%CI: 0.462-2.616; P=0.031) and clinical-radiomic nomogram-based LNM (HR=2.232, 95%CI:1.238-7.439; P=0.017) were independent risk factors for assessing 3-year RFS. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed good performance in training cohort (AUC=0.748), internal validation cohort (AUC=0.706) and external validation (AUC=0.688) for predicting 3-year RFS.ConclusionA clinical-radiomics nomogram exhibits good performance for predicting preoperative LNM. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed clinical potential for assessing 3-year RFS.

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Evaluation of parametrial infiltration in patients with IB‐IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in ¹⁸F‐fluorodeoxy glucose positron emission tomography/MR images

et al. 2023

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Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB‐IIB cervical cancer using features from 18F‐fluorodeoxy glucose (18F‐FDG) positron emission tomography (PET)/MR images. In this retrospective study, 66 patients with International Federation of Gynecology and Obstetrics stage IB‐IIB cervical cancer (22 with PMI and 44 without PMI) who underwent 18F‐FDG PET/MRI were divided into a training dataset (n = 46) and a testing dataset (n = 20). Features were extracted from both the tumoral and peritumoral regions in 18F‐FDG PET/MR images. Single‐modality and multimodality radiomics models were developed with random forest to predict PMI. The performance of the models was evaluated with F1 score, accuracy, and area under the curve (AUC). The Kappa test was used to observe the differences between PMI evaluated by radiomics‐based models and pathological results. The intraclass correlation coefficient for features extracted from each region of interest (ROI) was measured. Three‐fold crossvalidation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from the tumoral region in T2‐weighted images (F1 score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, p = 0.329) and the peritumoral region in PET images (F1 score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, p = 0.202) achieved the best performances in the testing dataset among the four single‐ROI radiomics models. The combined model using features from the tumoral region in T2‐weighted images and the peritumoral region in PET images achieved the best performance (F1 score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, p < 0.05). The results suggest that 18F‐FDG PET/MRI can provide complementary information regarding cervical cancer. The radiomics‐based method integrating features from the tumoral and peritumoral regions in 18F‐FDG PET/MR images gave a superior performance for evaluating PMI.

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MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

Cited by 32 publications

References 31 publications

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer

Evaluation of parametrial infiltration in patients with IB‐IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in ¹⁸F‐fluorodeoxy glucose positron emission tomography/MR images

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MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

Cited by 32 publications

References 31 publications

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer

Evaluation of parametrial infiltration in patients with IB‐IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18F‐fluorodeoxy glucose positron emission tomography/MR images

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Evaluation of parametrial infiltration in patients with IB‐IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in ¹⁸F‐fluorodeoxy glucose positron emission tomography/MR images